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A new trend tends to deploy deep learning algorithms to edge environments to mitigate privacy and latency issues from cloud computing. Diverse edge deep learning accelerators are devised to speed up the inference of deep learning algorithms on edge devices. Various edge deep learning accelerators feature different characteristics in terms of power and performance, which make it a very challenging task to efficiently and uniformly compare different accelerators. In this paper, we introduce EDLAB, an end-to-end benchmark, to evaluate the overall performance of edge deep learning accelerators. EDLAB consists of state-of-the-art deep learning models, a unified workload preprocessing and deployment framework, as well as a collection of comprehensive metrics.In addition, we propose parameterized models to model the hardware performance bound so that EDLAB can identify the hardware potentials and the hardware utilization of different deep learning applications. Finally, we employ EDLAB to benchmark three edge deep learning accelerators and analyze the benchmarking results. From the analysis we obtain some insightful observations that can guide the design of efficient deep learning applications.

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A neuronal recording system for brain-machine interfaces (BMI) based on asynchronous biphasic pulse coding is described. It demonstrates the first step in the development of a complete implanted wireless solution with fully integrated circuit architecture. A recording experiment comparing in parallel a commercial recording system (Tucker-Davis Technology (TDT)) and the UF's custom solution (FWIRE) is set up to compare performance. The novel aspect of the UF system is that the analog signal is represented by an asynchronous pulse train, which provides a low-power, low-bandwidth, noiseresistant means for coding and transmission. Taking advantage of neural firing features, the pulse-based approach uses only 3K pulses/second to record a 25 kHz bandwidth signal from a hardware neural simulator.I.